RoutIR: Scalable Routing & Retrieval Toolkit

• RoutIR is a suite of advanced methodologies and toolkits designed for dynamic retrieval pipelines, interference-aware routing in RIS networks, and interpretable LLM routing via IRT.
• It employs flexible, asynchronous batching and caching strategies to maximize GPU efficiency and reduce query latency in large-scale systems.
• RoutIR leverages rigorous optimization techniques, including MILP and IRT-based models, to balance throughput, cost, and network resiliency in diverse environments.

RoutIR denotes multiple advanced methodologies and toolkits for routing, retrieval, or resource assignment, with each usage domainally specific but marked by rigorous optimization of throughput, interpretability, and efficiency. In current literature, “RoutIR” is established most prominently in two distinct but technically significant contexts: high-throughput retrieval pipeline serving for neural retrieval–augmented generation (RAG) pipelines, and throughput-maximizing routing with interference avoidance in Reconfigurable Intelligent Surface (RIS)-assisted relay mesh networks. Related systems, such as IRT-Router (sometimes stylized “RoutIR”), extend item response theory to interpretable multi-LLM routing. The following summarizes the principal RoutIR implementations, technical underpinnings, and empirical evidence.

1. Retrieval Pipeline Serving: RoutIR for RAG Systems

RoutIR (Yang et al., 15 Jan 2026) is a Python-based package supporting fast, online serving of complex retrieval pipelines, central to Retrieval-Augmented Generation (RAG). Unlike traditional information retrieval (IR) toolkits tied to the Cranfield paradigm (fixed batch queries, offline processing), RoutIR exposes a flexible HTTP API for dynamic, on-the-fly composition and orchestration of retrieval pipelines needed by modern systems with multi-round, feedback-driven query flows or agentic behaviors.

System Architecture

RoutIR is organized into three core abstractions:

• Engine: Abstract interface supporting asynchronous batch search, reranking, rewriting, and fusion methods; Engines can be local (e.g., FAISS bi-encoder) or remote (accessed via a RelayEngine).
• Processor: Manages asynchronous batching, queueing, and caching for an underlying Engine. Each Processor maintains an in-memory queue, flushing batches either upon reaching a predefined batch_size or when a max_wait_time is exceeded.
• Pipeline: Compositional arrangement (Directed Acyclic Graph) of Processors, constructed on-demand via a domain-specific pipeline string grammar (e.g., "{qwen3-neuclir,plaidx-neuclir}RRF%50>>rank1"). This enables complex multi-stage retrieval with arbitrary fusion, reranking, and expansion operators.

Client queries are issued via HTTP POST with a simple JSON schema specifying the pipeline, collection, query, and result limit. The end-to-end system supports cached retrieval, concurrent execution, and result merging, returning final rankings as JSON maps.

Key Components and Operational Model

• Asynchronous Query Batching: Each Processor aggregates requests, maximizing batch size subject to latency constraints and batching them for efficient GPU or multithreaded execution. Cached results are served without re-invocation.
• Result Caching: Uses in-process LRU by default, with Redis support for multi-process scenarios. Caching is keyed by canonicalized JSON requests, optimizing repeated queries in agentic or multi-turn RAG workflows.
• Extensibility: Engines are Python subclasses implementing an async search_batch method, allowing rapid integration of custom retrieval, fusion, or reranking logic.

Supported Retrieval Techniques

Out-of-the-box engines include:

• Dense retrieval via FAISS,
• Multi-vector dense (PLAID-X),
• Sparse (BM25, MILCO/Anserini),
• Rerankers (monoT5, Qwen3, Rank1),
• Reciprocal Rank Fusion (RRF),
• Query expansion (docT5query).

Engines can be composed flexibly, and federated retrieval is facilitated via the RelayEngine abstraction.

2. Performance and Benchmarking of RoutIR

RoutIR’s runtime is benchmarked on the TREC 2023 NeuCLIR MLIR task (76 queries, ~10 million documents). Key observed statistics include:

Model	nDCG@20	Batched Throughput (q/s)	Sequential Latency (s/q)
PLAID-X	0.402	7.05	0.24
MILCO	0.413	3.27	2.46
Qwen3 (FAISS-PQ)	0.430	9.60	1.23

Batching consistently increases throughput. FAISS-based dense retrieval achieves maximal throughput by leveraging vectorized GPU operations. The amortized end-to-end per-query latency follows

$$T_\mathrm{total} = T_\mathrm{wait} + \frac{T_\mathrm{engine}(\mathrm{batch\ size})}{\mathrm{batch\ size}} + \epsilon_\mathrm{overhead}$$

with optimal trade-off between batch size (GPU utilization) and tail latency determined by application requirements.

3. Deployment, Scaling, and Fault Tolerance

RoutIR is designed for high availability and scalability:

• Horizontal Scaling: Multiple RoutIR instances with distributed assignment of Engine responsibilities, coordinated via external load balancing or Rendezvous hashing among RelayEngines.
• Failure Isolation: Blocking or critical Engines are served in separate RoutIR processes. System integrity is maintained—failure of a single engine does not halt others; missing pipeline legs return client-visible errors.
• Caching: Redis as a shared backend enables resilient, low-latency caching across cluster replicas.
• Security and Productionization: Lacks first-party authentication or rate-limiting; recommended to deploy under network-level authentication proxies or service meshes (e.g., Istio).

Fine-tuning max_wait_time and per-Engine batch_size allows practitioners to balance throughput, batch GPU execution efficiency, and tail response latency.

4. Interference-Aware Routing: RoutIR in RIS-Assisted Relay Mesh Networks

A discrete RoutIR methodology is presented for throughput maximization in wireless relay mesh networks with RIS-assisted communications (Phung et al., 2024), targeting next-generation THz-band, cell-less, and high-density environments.

Network and Channel Model

• Graph Structure: Network topology 𝒢=(ℕ,ℰ) comprises base stations (BS), passive RIS nodes (arrays with N reflecting elements), relay nodes (RN, performing decode-and-forward), and user equipment (UE, receivers).
• Multi-Hop Paths: Arbitrary combinations of BS, RIS, and RN are permitted, with paths structured as (b→r₁→...→r_h→u).
• Channel Model: THz narrowbeam, LoS-optimized, with detailed geometric modeling of conic and cylindrical beams, accounting for RIS placement, beamwidth α, and RIS element activation. Received power and SNR are derived from phased beam propagation and RIS parameters.

Interference Model

• Beam Overlap: Interference at a node (RIS, RN, UE) occurs when concurrent transmission volumes overlap. On RIS, jointly illuminated elements N_ι determine the intensity.
• Interference Power: For each interfering transmission k: $$I_{k\to j} = P_k \cdot |N_\iota|^2 \cdot |h_{k\to j}|^2 \cdot G_k \cdot G_j$$
• Signal-to-Noise-plus-Interference Ratio (SNIR): $$\mathrm{SNIR}_{i\to j} = \frac{P_j G_i G_j}{kTW + I_j}$$ with $I_j$ as the aggregate interference power.

Optimization Problem

A Mixed Integer Linear Program (MILP) maximizes the throughput control coefficient λ, subject to path selection, flow conservation, scheduling (no-conflict sets), and physical link constraints:

$$\max_{\{f^P_\gamma\},\lambda} \ \lambda \quad \text{s.t. constraints on } f^P_\gamma, y_{(i\to j)}, \text{ scheduling, and link capacity}$$

where link capacity $C_{(i\to j)} = W\log_2(1+\mathrm{SNR}_{(i\to j)})$.

Transmission Scheduling Heuristic

A priority-ordered heuristic partitions transmissions into conflict sets $C_\mathbb{F}$, enforcing SNIR constraints for concurrent scheduling. The process iteratively assigns transmissions to sets, verifying SNIR constraints via the analytical interference framework.

Algorithm pseudocode (as specified in the original data) implements this conflict-aware grouping strategy, which is crucial for effective path assignment without violating SNR constraints.

Path Selection and Throughput

• Low-Interference Criterion: Candidate paths are selected to minimize overlap with active beams (geometric separation), leveraging RIS placement and beamwidth optimization.
• Trade-offs: More hops increase SNR but introduce more scheduling slots; shortest paths may suffer from high interference.
• Empirical Results: In simulated multi-user indoor scenarios, low-interference path routing (DDP-LI) approximately doubles achievable throughput versus shortest-path assignment (DDP-SP), especially in lightly loaded networks. Throughput gain persists at moderate and high loads.

5. Interpretability and Practical Considerations (IRT-Router Context)

While “RoutIR” in the retrieval and networking contexts emphasizes throughput and flexibility, the IRT-Router framework (Song et al., 1 Jun 2025) (sometimes referenced as “RoutIR”) provides interpretability and statically modeled routing decisions:

• Item Response Theory (IRT) Formulation: Utilizes a three-parameter logistic model to relate query traits and model ability.

$P_{i,j} = \frac{1}{1+\exp(-a_i(\theta_j - b_i))}$

with $a_i$ (discrimination), $b_i$ (difficulty), and $\theta_j$ (LLM ability).

• Routing Decision Rule: For each query, selects the LLM that maximizes the cost-performance trade-off

$$M^*(q_i) = \arg\max_{M_j}[\alpha P_{i,j} - \beta\mathcal{C}(M_j)]$$

• Online Query Warm-Up: Novel queries are “warmed up” via semantic similarity to training queries, improving OOD generalization.
• Interpretability: Parameters $\theta_j$, $b_i$, and $a_i$ provide explicit capability, difficulty, and sensitivity quantification, empirically correlating with model size, independent human labeling, and routing outcomes.

Empirically, this approach yields significant cost savings while maintaining or surpassing the accuracy of the largest models, with stable performance across both in-distribution and out-of-distribution datasets.

6. Summary and Comparative Table

RoutIR encapsulates a set of rigorous, extensible methodologies spanning high-throughput retrieval (pipeline serving), interference-aware routing (RIS mesh), and interpretable resource assignment (IRT-based LLM routing). Each approach shares a focus on efficiency, dynamic adaptation, and principled optimization, with distinctive technical strategies tailored to their domain.

Domain	RoutIR Functionality	Notable Properties
Retrieval Pipelines	Fast API-based, asynchronous, composable retrieval	Pipeline DSL; batch+cache; multi-engine
RIS Mesh Networks	Beam-geometry-aware, interference avoiding routing	MILP; interference modeling; scheduling
LLM Routing (IRT)	Interpretable route to optimal LLM	IRT-based scoring; cost/performance tradeoff

Each variant is underpinned by published theoretical models, operationalized in open-source toolkits or simulation frameworks, and benchmarked in rigorous empirical settings (Yang et al., 15 Jan 2026, Phung et al., 2024, Song et al., 1 Jun 2025).

A plausible implication is that the RoutIR design paradigm—combining expressible, modular orchestration with analytical throughput or interpretability models—will continue to provide a foundation for scalable, interpretable, and high-efficiency resource assignment in knowledge-intensive and networked systems.